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a
r
e
us
e
d
f
or
ke
y
w
o
r
d
s
tor
a
ge
.
T
h
e
s
o
l
u
t
i
o
n
f
or
t
h
e
s
a
m
e
c
a
n
b
e
e
x
ten
de
d
by
us
i
n
g
i
n
d
e
xi
n
g
f
or
b
ot
h
ke
y
w
or
d
a
n
d
s
p
a
t
i
a
l
di
s
t
r
i
b
u
t
i
o
n
s
i
n
th
e
w
or
kl
o
a
d
o
f
q
ue
r
i
e
s
f
or
hi
gh
e
r
e
f
f
i
c
i
e
n
c
y
.
C
h
e
n
e
t
a
l.
[
3
]
t
h
e
m
e
t
h
o
d
o
f
i
n
v
e
r
te
d
i
n
de
xi
n
g
i
s
us
e
d
,
b
u
t
th
e
s
e
a
r
e
n
ot
e
f
f
i
c
i
e
n
t
f
or
tex
tual
f
i
l
ter
i
n
g
a
s
t
h
e
p
r
ob
l
e
m
i
n
c
o
n
s
i
de
r
a
t
i
o
n
i
s
a
s
ub
s
e
t
c
o
n
tai
nm
e
n
t
s
e
a
r
c
h
i
n
a
tex
tual
o
u
t
l
ook
.
E
v
e
n
t
h
ou
gh
t
h
e
i
nv
e
r
te
d
i
n
de
xi
n
g
tec
h
ni
que
s
a
r
e
e
s
tabl
i
s
h
e
d
i
n
s
pa
t
i
a
l
-
ke
y
w
or
d
q
ue
r
y
i
n
g,
t
h
e
y
a
r
e
m
a
i
nl
y
c
o
n
s
i
de
r
e
d
wi
t
h
s
up
e
r
s
e
t
c
o
n
ta
i
nm
e
n
t
que
r
i
e
s
[
5]
,
i
n
wh
i
c
h
a
l
l
q
ue
r
y
ke
y
w
o
r
ds
c
o
n
tai
n
e
d
wi
t
hi
n
i
n
de
x
e
d
o
bj
e
c
t
s
a
r
e
r
e
tr
i
e
v
e
d
.
E
n
h
a
n
c
e
m
e
n
t
i
n
p
e
r
f
or
m
a
n
c
e
i
s
vi
s
i
bl
e
wh
e
n
t
h
e
ke
y
w
or
ds
’
or
d
e
r
i
n
g
i
s
h
a
n
d
l
e
d
a
nd
c
o
m
bi
n
a
t
i
o
n
s
o
f
m
u
l
t
i
p
l
e
ke
y
w
or
ds
a
r
e
i
n
de
x
e
d
.
T
h
i
s
c
a
n
b
e
s
e
e
n
i
n
tec
h
ni
que
s
de
f
i
n
e
d
f
or
q
ue
r
i
e
s
f
or
s
up
e
r
s
e
t
c
on
ta
i
nm
e
n
t
us
i
n
g
t
h
e
or
d
e
r
e
d
ke
y
wor
d
tr
i
e
[
6]
.
Ga
l
i
ć
e
t
al.
[
7]
pr
e
s
e
n
t
e
d
a
f
r
a
m
e
wo
r
k
t
h
a
t
c
on
s
i
s
t
s
o
f
t
he
t
y
pe
s
o
f
da
t
a
a
n
d
t
h
e
o
pe
r
a
t
i
o
ns
s
uppo
r
t
i
n
g
s
pa
t
i
a
l
-
s
t
r
e
a
m
i
ng
da
t
a
.
A
s
pa
t
i
a
l
-
t
e
m
p
o
r
a
l
l
a
n
gua
g
e
f
o
r
que
r
i
e
s
i
s
d
i
s
c
u
s
s
e
d
[
8]
f
o
r
pr
o
c
e
s
s
i
ng
ge
o
-
s
t
r
e
a
m
i
ng
da
t
a
.
A
d
i
s
t
r
i
b
ut
e
d
f
r
a
m
e
wo
r
k
u
n
de
r
ge
o
-
s
tr
e
a
m
s
i
s
de
ve
l
o
pe
d
[
9]
,
[
10
]
f
o
r
e
f
f
i
c
i
e
n
t
ly
s
upe
r
vi
s
i
ng
m
o
t
i
l
e
da
t
a
o
bj
e
c
t
s
us
i
ng
d
i
s
t
r
i
b
ut
e
d
ge
o
-
s
t
r
e
a
m
i
ng
da
t
a
pr
o
c
e
s
s
i
n
g
i
n
m
a
s
s
i
ve
c
l
u
s
t
e
r
s
whil
e
in
r
e
a
l
-
t
i
m
e
.
Al
t
h
o
ugh
a
ll
t
h
e
s
e
li
t
e
r
a
r
y
wo
r
ks
a
r
e
ba
s
e
d
o
nly
o
n
t
h
e
G
e
o
S
t
r
e
a
m
s
’
s
pa
t
i
a
l
d
im
e
ns
i
o
n
,
t
h
e
r
e
i
s
a
l
s
o
a
p
r
e
s
e
n
c
e
o
f
t
e
x
t
ua
l
i
n
f
o
r
m
a
t
i
o
n
i
n
ge
o
s
t
r
e
a
m
s
.
I
n
f
o
r
c
e
,
l
a
r
ge
a
m
o
un
t
s
o
f
ge
o
-
s
pa
t
i
a
l
da
t
a
ge
n
e
r
a
t
e
d
r
e
c
e
n
t
l
y
a
l
s
o
i
n
c
l
ude
ge
o
-
t
a
gge
d
m
i
c
r
o
-
bl
o
gs
,
po
i
n
t
s
o
f
i
n
t
e
r
e
s
t
(
P
OI
s
)
,
i
m
a
ge
s
c
o
n
t
a
i
ni
ng
tags
a
n
d
ge
o
-
l
o
c
a
t
i
o
n
s
[
3]
,
[
11]
.
A
s
pe
r
t
h
e
i
nf
o
r
m
a
t
i
o
n
i
n
[
12]
,
a
ppr
o
x
i
m
a
t
e
ly
30
mi
ll
i
o
n
us
e
r
s
s
u
bmi
t
t
e
d
da
t
a
wi
t
h
ge
o
-
t
a
gs
o
n
T
w
i
t
t
e
r
.
Al
s
o
,
m
uc
h
s
uc
h
s
o
f
t
wa
r
e
li
ke
s
o
c
i
a
l
n
e
t
wo
r
ki
n
g
s
i
t
e
s
(
F
a
c
e
b
o
o
k
a
n
d
T
w
i
t
t
e
r
)
a
n
d
r
e
g
i
o
n
a
l
s
e
r
vi
c
e
s
(
r
e
g
i
o
na
l
a
d
v
e
r
t
i
s
i
ng)
s
e
n
d
da
t
a
i
n
a
r
a
p
i
d
s
t
r
e
a
m
i
ng
pa
t
t
e
r
n
[
12]
.
T
h
e
hy
b
r
i
d
i
nde
xi
ng
f
o
r
t
h
e
ge
o
-
s
pa
t
i
a
l
k
-
n
e
a
r
e
s
t
n
e
i
g
hb
o
r
(
kNN
)
que
r
i
e
s
i
s
d
i
s
c
us
s
e
d
[
13]
.
T
h
e
a
ut
h
or
s
us
e
d
t
h
e
t
r
e
e
a
ppr
oa
c
h
a
n
d
t
h
e
s
pa
c
e
-
f
il
li
ng
c
ur
v
e
c
o
n
c
e
pt
f
o
r
i
n
d
e
xi
n
g
t
h
e
q
ue
r
i
e
s
[
14]
.
Va
r
i
o
us
s
im
il
a
r
i
t
y
m
e
a
s
ur
e
s
t
o
f
i
n
d
t
he
ne
a
r
e
s
t
o
bj
e
c
t
s
a
r
e
d
i
s
c
us
s
e
d
[
15]
.
T
h
e
a
ut
h
o
r
s
s
ugge
s
t
e
d
a
hy
br
i
d
f
r
a
m
e
wo
r
k
c
o
m
bi
n
i
ng
R
-
t
r
e
e
f
o
r
l
o
c
a
t
i
o
n
da
t
a
a
n
d
t
h
e
i
nve
r
t
e
d
f
i
l
e
f
o
r
ke
y
wo
r
d
pur
p
o
s
e
[
16
]
.
A
ne
w
hy
br
i
d
i
nde
xi
ng
m
e
t
h
o
d,
a
da
pt
i
v
e
g
e
o
-
t
e
x
t
ua
l
(
s
p
a
t
i
a
l
-
t
e
x
t
ua
l
)
pa
r
t
i
t
i
o
n
t
r
e
e
(
A
P
-
T
r
e
e
)
,
i
s
c
o
i
n
e
d
t
o
e
f
f
i
c
i
e
n
t
l
y
m
a
n
a
g
e
c
o
n
t
i
n
uo
us
a
n
d
d
y
na
mi
c
s
pa
t
i
a
l
-
ke
y
wo
r
d
que
r
i
e
s
.
An
A
P
-
T
r
e
e
c
a
n
b
e
d
e
f
i
ne
d
a
s
a
n
f
-
a
r
y
t
r
e
e
i
n
w
hi
c
h
t
h
e
r
e
i
s
a
r
e
c
ur
s
i
ve
d
i
v
i
s
i
o
n
o
f
que
r
i
e
s
b
a
s
e
d
o
n
ke
y
wo
r
d
o
r
s
pa
t
i
a
l
pa
r
t
i
t
i
o
n
s
(
n
o
de
s
)
.
A
c
o
s
t
m
o
de
l
i
s
a
l
s
o
pr
o
p
o
s
e
d
to
l
o
o
k
o
v
e
r
t
h
e
a
s
s
o
r
t
m
e
n
t
o
f
d
i
vi
s
i
o
n
t
e
c
hni
qu
e
s
s
o
t
h
a
t
t
h
e
i
n
d
e
xi
ng
i
s
f
l
e
xi
b
l
e
w
i
t
h
t
h
e
que
r
y
wo
r
kl
o
a
d
by
i
n
t
e
gr
a
t
i
n
g
a
n
a
l
t
e
r
na
t
i
ve
f
o
r
m
o
f
o
r
de
r
e
d
ke
y
wo
r
d
tr
i
e
s
t
r
uc
t
ur
e
to
im
pr
o
v
e
t
h
e
pe
r
f
o
r
m
a
n
c
e
o
f
t
e
x
t
ua
l
f
il
t
e
r
i
ng
[
12]
.
No
n
e
t
h
e
l
e
s
s
,
t
h
e
i
n
de
xi
ng
f
o
r
c
o
n
t
i
n
uo
us
s
pa
t
i
a
l
-
ke
y
wo
r
d
que
r
i
e
s
m
e
t
h
o
ds
f
a
c
e
s
t
wo
f
u
n
da
m
e
n
t
a
l
pr
o
bl
e
m
s
i
n
t
h
e
pr
e
s
e
n
t
s
c
e
na
r
i
o
.
F
i
r
s
t
l
y
,
t
h
e
s
e
i
nde
xi
ng
a
p
pr
o
a
c
h
e
s
do
n
ot
t
a
ke
i
n
t
o
a
c
c
o
un
t
pa
r
t
i
a
l
ke
y
wo
r
d
m
a
t
c
hi
ng.
I
n
m
a
ny
a
pp
li
c
a
t
i
o
ns
,
e
x
a
c
t
s
t
r
i
n
g
m
a
t
c
hi
n
g
do
e
s
n
o
t
c
a
t
e
r
to
t
h
e
n
e
e
d.
P
a
r
t
i
a
l
o
r
f
u
z
z
y
ke
y
wo
r
d
m
a
t
c
hi
ng
i
s
r
e
qu
i
r
e
d
f
o
r
us
e
r
s
wh
o
do
n
ot
h
a
v
e
a
c
l
e
a
r
s
e
a
r
c
h
c
o
n
d
i
t
i
o
n
,
o
r
s
o
m
e
ke
y
wo
r
d
e
r
r
o
r
s
(
s
pe
l
li
ng
e
r
r
o
r
s
)
,
o
r
w
h
e
n
t
he
que
r
i
e
d
da
t
a
i
t
s
e
lf
h
a
s
s
o
m
e
s
o
r
t
o
f
u
n
r
e
li
a
bl
e
f
r
a
g
m
e
n
t
s
.
T
hi
s
f
e
a
t
ur
e
o
f
a
ppr
o
xi
m
a
t
e
ke
y
wo
r
d
m
a
t
c
hi
n
g
i
s
ne
c
e
s
s
a
r
y
f
o
r
t
h
e
pr
o
c
e
s
s
i
n
g
o
f
ge
o
s
pa
t
i
a
l
-
b
a
s
e
d
da
t
a
,
a
f
f
i
r
m
e
d
by
t
h
e
s
t
ud
i
e
s
i
n
[
17]
.
T
h
e
pr
e
vi
o
us
wo
r
k
f
o
r
s
uc
h
t
y
p
e
o
f
que
r
y
pr
o
c
e
s
s
i
ng
f
o
c
us
e
d
o
n
m
a
i
n
ly
e
x
a
c
t
ke
y
wo
r
d
m
a
t
c
hi
ng.
R
e
c
e
n
t
l
y
,
t
h
e
s
pa
t
i
a
l
-
k
e
y
w
or
d
s
e
a
r
c
h
h
a
s
b
e
e
n
a
s
i
gh
t
o
f
i
n
t
e
r
e
s
t,
whi
c
h
i
n
ten
ds
to
f
e
tch
t
h
e
r
e
l
a
t
e
d
s
pa
t
i
a
l
-
tex
t
u
a
l
o
b
j
e
c
t
s
f
or
a
s
ubm
i
tt
e
d
s
p
a
t
i
a
l
-
ke
y
w
or
d
q
ue
r
y
.
I
n
g
e
n
e
r
a
l
,
t
h
e
c
u
r
r
e
n
t
w
or
k
i
s
a
c
o
m
bin
a
t
i
o
n
o
f
s
pa
t
i
a
l
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n
de
xi
n
g
a
n
d
ke
y
w
o
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d
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n
de
xi
n
g
m
e
t
h
ods
to
c
o
n
s
tr
u
c
t
o
b
j
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c
t
s
s
o
th
a
t
i
n
c
o
m
pa
t
i
bl
e
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bj
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c
t
s
a
r
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f
f
i
c
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l
y
e
l
i
m
i
n
a
t
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d
f
r
o
m
t
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x
tual
a
n
d
s
pa
t
i
a
l
p
e
r
s
pe
c
t
i
v
e
s
.
B
r
oa
dl
y
,
t
h
e
s
e
m
e
t
h
ods
c
a
n
b
e
c
a
teg
or
i
z
e
d
i
n
to
tw
o
c
l
a
s
s
e
s
:
k
e
y
w
or
d
-
f
i
r
s
t
[
18
]
-
[
2
1]
a
n
d
s
pa
t
i
a
l
-
f
i
r
s
t
[
1
6
]
,
[
22
]
.
I
n
c
o
n
t
i
n
u
ous
q
ue
r
y
p
r
o
c
e
s
s
i
n
g
s
y
s
te
m
s
,
t
h
e
r
e
a
r
e
q
ui
te
a
f
e
w
e
n
dl
e
s
s
que
r
i
e
s
t
h
a
t
k
e
e
p
o
n
r
un
ni
n
g
c
o
n
t
i
n
u
o
us
l
y
.
T
h
e
i
n
c
o
m
i
n
g
da
ta
o
b
j
e
c
t
s
a
r
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e
v
a
l
ua
t
e
d
c
o
n
s
tan
t
l
y
a
nd
a
s
s
i
gn
e
d
to
th
e
m
a
t
c
h
e
d
q
ue
r
i
e
s
l
ogge
d
i
n
t
h
e
s
y
s
t
e
m
.
M
ul
t
i
p
l
e
w
or
ks
o
n
p
ubl
i
s
h
/s
ub
s
c
r
i
b
e
m
o
de
l
s
e
x
pl
or
e
a
l
ot
o
f
c
o
n
t
i
n
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o
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s
que
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l
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ke
p
r
e
d
i
c
a
te
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b
a
s
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d
m
a
tchi
n
g
[
23
]
-
[
26
]
a
n
d
s
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m
i
l
a
r
i
t
y
-
b
a
s
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d
r
a
n
ki
n
g
[
27
]
,
[
28
]
.
How
e
v
e
r
,
s
pa
t
i
a
l
d
a
t
a
i
s
n
ot
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k
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n
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n
to
c
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n
s
i
de
r
a
t
i
o
n
.
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or
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e
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e
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l
y
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o
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t
i
n
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o
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s
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n
a
m
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p
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t
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a
l
k
e
y
w
or
d
que
r
i
e
s
a
r
e
un
de
r
s
tu
d
y
[
29
]
,
[
30
]
,
h
owe
v
e
r
,
t
h
e
y
l
ook
i
n
to
c
o
n
t
i
n
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ous
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d
d
r
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s
i
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g
o
f
a
pp
r
op
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te
da
ta
o
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j
e
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t
s
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h
dy
n
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m
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c
q
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,
m
a
ki
n
g
i
t
f
un
da
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ta
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l
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n
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o
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pa
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s
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owe
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d
top
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k
s
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m
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d
[
31
]
.
H
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,
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t
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x
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or
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s
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h
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m
o
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t
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tex
tual
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tec
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tor
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a
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ke
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y
d
a
ta
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j
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t
to
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x
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u
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s
p
a
t
i
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l
-
k
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y
w
or
d
q
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r
i
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s
.
T
he
d
a
ta
s
tr
u
c
tu
r
e
R
-
tr
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h
a
s
b
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l
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oun
d
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tex
tu
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F
or
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x
a
m
p
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,
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h
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or
k
[
32
]
,
t
h
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a
u
t
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or
s
l
ook
e
d
i
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to
a
hy
b
r
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nv
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r
te
d
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oc
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tok
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d
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d
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R
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da
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tex
tual
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.
A
l
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th
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s
a
m
e
l
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s
,
Z
h
a
n
g
e
t
a
l
.
[
3
3
]
p
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f
or
w
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(
I
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[
20
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f
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i
e
s
.
T
o
ta
c
k
l
e
t
h
i
s
,
t
h
e
a
u
t
h
or
s
p
r
op
o
s
e
d
a
n
a
da
p
t
i
ve
tex
tu
a
l
-
s
pa
t
i
a
l
p
a
r
t
i
t
i
o
n
tr
e
e
da
ta
s
tr
uc
tu
r
e
i
.
e
.
,
AP
-
tr
e
e
[
12
]
u
t
i
l
i
z
e
s
a
n
f
-
a
r
y
d
a
ta
s
tr
u
c
tur
e
th
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t
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o
c
e
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th
e
i
n
de
xi
n
g
o
f
t
h
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q
ue
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i
n
a
f
l
e
xi
bl
e
w
a
y
c
o
n
s
i
de
r
i
n
g
t
h
e
w
or
kl
oa
d
o
f
qu
e
r
i
e
s
.
B
u
t
th
e
pr
o
b
l
e
m
l
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e
s
i
n
t
h
e
f
a
c
t
th
a
t
th
e
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ur
r
e
n
t
i
n
de
xi
n
g
tec
h
ni
que
s
f
or
c
o
n
t
i
n
u
ou
s
a
n
d
dy
n
a
m
i
c
s
pa
t
i
a
l
-
ke
y
w
or
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q
ue
r
i
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d
o
n
ot
s
up
p
or
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a
pp
r
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i
m
a
te
or
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r
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i
a
l
k
e
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w
or
d
s
e
a
r
c
h
e
s
.
T
h
e
a
u
t
h
or
s
p
u
t
f
or
t
h
a
n
M
HR
T
r
e
e
i
.
e
.
,
R
T
r
e
e
us
i
n
g
a
m
i
n
-
wi
s
e
s
i
gn
a
t
u
r
e
wi
t
h
l
i
n
e
a
r
h
a
s
hi
n
g
wh
i
c
h
c
o
m
bi
n
e
s
t
h
e
R
-
tr
e
e
a
n
d
M
i
n
-
w
i
s
e
s
i
gn
a
t
u
r
e
to
e
x
e
c
u
te
s
pa
t
i
a
l
k
e
y
w
or
d
q
ue
r
i
e
s
wh
e
r
e
t
h
e
obj
e
c
t
s
a
r
e
s
e
a
r
c
h
e
d
f
or
a
pp
r
o
x
i
m
a
t
e
s
tr
i
n
g
m
a
t
c
hi
n
g
[
17
]
.
T
h
e
a
u
t
h
or
s
us
e
d
th
e
a
dv
a
n
c
e
d
a
da
p
t
i
v
e
pa
r
t
i
t
i
o
ni
n
g
tr
e
e
c
o
n
c
e
p
t
i
n
whi
c
h
q
-
g
r
a
m
s
’
s
i
gn
a
tu
r
e
s
a
r
e
s
tor
e
d
i
n
a
n
a
dv
a
n
c
e
d
a
da
p
t
i
v
e
p
a
r
t
i
t
i
o
n
tr
e
e
-
A
A
P
tr
e
e
(
A
P
-
T
r
e
e
+
)
.
T
h
e
dy
n
a
m
i
c
p
r
og
r
a
m
m
i
n
g
a
pp
r
oa
c
h
i
s
us
e
d
to
di
vi
d
i
n
g
ke
y
w
o
r
ds
i
n
to
di
f
f
e
r
e
n
t
p
a
r
t
i
t
i
o
n
s
[
35
]
.
T
h
e
s
ub
-
q
ue
r
i
e
s
a
r
e
ge
n
e
r
a
te
d
f
r
o
m
c
o
n
t
i
n
u
ous
qu
e
r
i
e
s
u
s
i
n
g
a
s
i
m
u
l
a
t
e
d
a
n
n
e
a
l
i
n
g
a
l
g
or
i
t
hm
.
T
h
e
p
us
h
a
n
d
p
u
l
l
-
b
a
s
e
d
da
ta
di
s
s
e
m
i
n
a
t
i
o
n
a
pp
r
oa
c
h
e
s
a
r
e
us
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d
to
s
e
n
d
c
h
a
n
ge
s
to
th
e
us
e
r
a
s
pe
r
t
h
e
us
e
r
’
s
i
n
ter
e
s
t
[
36
]
.
A
s
i
gni
f
i
c
a
n
t
a
m
o
un
t
o
f
tem
p
or
a
l
da
ta
i
s
ge
n
e
r
a
te
d
i
n
n
e
tw
or
k
m
o
ni
tor
i
n
g,
a
n
d
s
toc
k
e
x
c
h
a
n
ge
.
W
hi
c
h
c
a
n
b
e
us
e
d
in
o
nl
i
n
e
de
c
i
s
i
o
n
m
a
ki
n
g
a
s
d
y
n
a
m
i
c
da
ta
i
t
e
m
s
a
r
e
g
e
n
e
r
a
ted
[
3
7
]
.
A
p
r
e
s
or
te
d
-
n
e
a
r
e
s
t
i
n
de
x
tr
e
e
a
l
g
or
i
t
h
m
i
s
i
n
tr
oduc
e
d
f
or
n
e
a
r
e
s
t
n
e
i
gh
b
or
qu
e
r
i
e
s
o
n
m
o
bi
l
e
ob
j
e
c
t
s
w
i
t
hi
n
d
e
s
i
r
e
d
pe
r
i
o
d
a
n
d
ou
tpe
r
f
or
m
e
d
i
n
s
a
vi
n
g
t
i
m
e
c
o
m
pa
r
e
d
to
K
D
-
tr
e
e
a
pp
r
oa
c
h
[
3
8]
.
Va
n
t
a
ge
p
o
i
n
t
tr
e
e
i
n
de
xi
n
g
w
i
t
h
s
pe
c
tr
a
l
c
l
us
te
r
i
n
g
i
s
us
e
d
f
or
hi
gh
di
m
e
n
s
i
o
n
a
l
da
ta
f
or
e
f
f
e
c
t
i
v
e
da
ta
r
e
tr
i
e
v
a
l
f
or
u
s
e
r
que
r
y
[
39
]
.
T
h
e
a
u
t
h
or
s
di
s
c
us
s
e
d
v
a
r
i
o
us
r
e
a
l
t
i
m
e
c
l
a
s
s
i
f
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c
a
t
i
o
n
a
n
d
c
l
us
t
e
r
i
n
g
tec
h
ni
que
s
f
o
r
da
ta
s
tr
e
a
m
a
s
w
e
l
l
a
s
pl
a
t
f
or
m
s
f
or
m
i
ni
n
g
o
f
d
a
ta
s
tr
e
a
m
s
[
40
]
.
A
n
e
w
f
r
a
m
e
w
or
k
f
o
r
l
oc
a
t
i
o
n
-
b
a
s
e
d
s
e
r
vi
c
e
s
un
de
r
th
e
u
m
b
r
e
l
l
a
o
f
m
o
bi
l
e
c
l
o
u
d
c
o
m
pu
t
i
n
g
p
r
o
vi
di
n
g
t
h
e
l
o
c
a
t
i
o
n
p
r
i
v
a
c
y
a
n
d
i
n
teg
r
i
t
y
o
f
s
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r
vi
c
e
r
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s
u
l
t
s
i
s
di
s
c
us
s
e
d
[
4
1]
.
As
t
h
e
i
nc
o
m
i
n
g
que
r
y
da
t
a
i
s
m
a
s
s
i
ve
i
n
nu
m
be
r
,
t
h
e
ne
e
d
t
o
c
o
m
e
up
w
i
t
h
e
f
f
e
c
t
i
v
e
i
nde
xi
n
g
t
e
c
h
ni
que
s
s
o
t
h
a
t
qui
t
e
a
f
e
w
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n
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o
m
p
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bl
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qu
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s
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a
n
b
e
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f
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ne
d
a
t
a
n
o
m
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na
l
c
o
s
t
h
a
s
be
c
o
m
e
vi
t
a
l
.
I
n
t
hi
s
pa
pe
r
,
f
o
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h
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pr
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e
s
s
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n
g
o
f
s
pa
t
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a
l
ke
y
wo
r
d
t
e
m
po
r
a
l
r
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g
i
o
n
que
r
i
e
s
,
a
L
e
v
e
ns
h
t
e
i
n
d
i
s
t
a
n
c
e
a
da
pt
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ve
p
a
r
t
i
t
i
o
n
t
r
e
e
(
L
D
A
P
t
r
e
e
)
i
s
pr
o
po
s
e
d.
I
n
t
h
e
L
DA
P
t
r
e
e
,
t
h
e
ke
y
wo
r
ds
a
r
e
s
to
r
e
d
f
o
r
a
ppr
o
xi
mat
e
s
tr
i
n
g
m
a
t
c
hi
ng.
A
l
o
c
a
ll
y
o
pt
i
m
a
l
m
e
t
h
o
d
i
s
us
e
d
f
o
r
ke
y
wo
r
d
pa
r
t
i
t
i
o
ni
ng
a
n
d
t
h
e
L
e
v
e
ns
h
t
e
i
n
e
d
i
t
d
i
s
t
a
nc
e
c
o
n
c
e
pt
i
s
us
e
d
f
o
r
a
ppr
o
xi
m
a
t
e
s
t
r
i
n
g
m
a
t
c
hi
n
g.
T
h
e
pa
pe
r
e
m
p
h
a
s
i
z
e
s
t
h
e
c
ha
l
l
e
nge
s
o
f
a
pp
r
o
xi
m
a
t
e
ke
y
wo
r
d
s
e
a
r
c
hi
ng
a
n
d
pr
o
vi
d
i
ng
a
n
e
f
f
i
c
i
e
n
t
s
o
l
ut
i
o
n
f
o
r
s
tr
e
a
m
i
ng
da
t
a
.
2.
P
R
O
P
O
S
E
D
M
E
T
H
O
D
F
o
r
t
h
e
pr
o
c
e
s
s
i
n
g
o
f
s
pa
t
i
a
l
ke
y
wo
r
d
t
e
m
po
r
a
l
r
e
g
i
o
n
que
r
i
e
s
,
a
L
D
A
P
t
r
e
e
i
s
pr
o
po
s
e
d
c
o
n
t
a
i
ni
ng
ke
y
wo
r
d
n
o
de
s
,
s
pa
t
i
a
l
n
o
de
s
a
n
d
que
r
y
n
o
de
s
.
A
l
o
c
a
l
ly
o
pt
i
m
a
l
gr
e
e
d
y
m
e
t
h
o
d
i
s
us
e
d
f
o
r
ke
y
wo
r
d
pa
r
t
i
t
i
o
ni
ng
o
n
ke
y
wo
r
d
n
o
de
.
I
n
L
DA
P
t
r
e
e
,
t
h
e
ke
y
wo
r
ds
a
r
e
s
tor
e
d
f
o
r
a
ppr
o
xi
m
a
t
e
s
tr
i
n
g
m
a
t
c
hi
n
g.
2.
1.
P
r
ob
lem
f
o
r
m
u
l
at
ion
T
h
e
b
a
s
i
c
s
o
f
s
p
a
t
i
a
l
k
e
y
w
or
d
tem
p
or
a
l
da
ta
s
tr
e
a
m
a
n
d
s
pa
t
i
a
l
ke
y
wor
d
te
m
p
or
a
l
que
r
i
e
s
a
r
e
p
r
o
v
i
de
d
.
-
De
f
i
n
i
t
i
o
n
1:
S
pa
t
i
a
l
ke
y
wo
r
d
t
e
m
po
r
a
l
o
bj
e
c
t
i
s
de
f
i
ne
d
a
s
O
=
(
i
d
,
t
,
l
o
c
,
kws
)
w
h
e
r
e
t
i
s
a
t
i
m
e
s
t
a
m
p
a
n
d
l
o
c
i
s
a
l
o
c
a
t
i
o
n
o
f
t
h
e
o
bj
e
c
t
.
T
h
e
s
e
t
o
f
ke
y
w
o
r
ds
a
s
s
o
c
i
a
t
e
d
w
i
t
h
t
h
e
o
b
j
e
c
t
i
s
de
n
o
t
e
d
by
kws
.
-
De
f
i
n
i
t
i
o
n
2:
S
pa
t
i
a
l
ke
y
wo
r
d
t
e
m
po
r
a
l
da
t
a
s
t
r
e
a
m
i
s
de
f
i
ne
d
a
s
S
=
{O
i
|
i
∈
[
1,
+
∞]
∧
O
i
.
t
≤
O
i
+
1
.
t
}
.
It
is
a
n
u
nb
o
un
de
d
s
e
t
o
f
s
pa
t
i
a
l
-
ke
y
wo
r
d
o
bj
e
c
t
s
i
n
i
nc
r
e
a
s
i
ng
t
i
m
e
s
t
a
m
p
o
r
de
r
.
-
De
f
i
n
i
t
i
o
n
3:
T
h
e
c
o
n
t
i
n
uo
us
s
p
a
t
i
a
l
-
ke
y
wo
r
d
t
e
m
po
r
a
l
que
r
y
i
s
de
n
o
t
e
d
a
s
q
=
(
i
d,
t
1
,
t
2
,
r
,
kw)
wh
e
r
e
t
1
a
n
d
t
2
a
r
e
t
h
e
t
i
m
e
s
t
a
m
p
s
dur
i
ng
w
hi
c
h
t
h
e
qu
e
r
y
e
xi
s
t
s
a
n
d
r
i
s
t
h
e
r
e
c
t
a
n
gu
l
a
r
r
e
g
i
o
n
o
f
t
h
e
que
r
y
.
T
h
e
kw
i
s
a
s
e
t
o
f
d
i
s
t
i
n
c
t
ke
y
wo
r
ds
a
s
s
o
c
i
a
t
e
d
w
i
t
h
t
h
e
que
r
y
.
A
S
pa
t
i
a
l
-
ke
y
wo
r
d
t
e
m
po
r
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a
r
e
di
vi
d
e
d
i
n
t
o
c
or
de
r
e
d
c
ut
s
a
c
c
o
r
di
n
g
t
o
t
h
e
i
n
de
x
o
f
t
h
a
t
ke
y
wo
r
d
i
n
t
h
e
que
r
y
a
n
d
t
h
a
t
i
n
de
x
w
il
l
de
c
i
d
e
t
h
e
pa
r
t
i
t
i
o
n
o
f
f
s
e
t
o
f
t
h
a
t
ke
y
wo
r
d
n
o
de
N.
E
a
c
h
c
ut
or
b
uc
ke
t
B
m
a
y
c
o
n
t
a
i
n
t
h
e
s
e
t
o
f
o
r
de
r
e
d
ke
y
wo
r
ds
C
[
Ki
,
Kj
]
w
h
e
r
e
Ki
a
n
d
Kj
a
r
e
k
e
y
wo
r
ds
f
o
r
l
e
f
t
a
n
d
r
i
g
h
t
b
o
un
da
r
i
e
s
i
n
t
h
e
g
i
ve
n
c
ut
.
F
o
r
di
vi
d
i
ng
t
h
e
ke
y
w
o
r
d
n
o
de
i
n
t
o
di
f
f
e
r
e
n
t
c
ut
s
,
a
c
o
s
t
m
o
de
l
i
s
u
s
e
d.
T
h
e
c
o
s
t
o
f
pa
r
t
i
t
i
o
n
P
i
s
c
o
m
put
e
d
a
s
[
12]
:
C
(
P
)
=
∑
w
(
Bi
)
∗
p
(
Bi
)
C
i
=
1
(
2)
w
he
r
e
C
:
nu
mb
e
r
o
f
c
u
t
s
a
nd
w
(
B
i
)
:
nu
mb
e
r
o
f
q
u
e
r
i
e
s
a
s
s
o
c
i
a
t
e
d
t
o
B
i
.
p(
B
)
=
∑
p
(
w
)
w
∈
B
(
3
)
p(
w)
=
f
r
e
q
(
w
)
∑
f
r
e
q
(
w
)
w
∈
P
(
4
)
p
(
B
)
=
a
r
e
a
(
B
)
/
a
r
e
a
(
N)
(
5)
-
I
f
q
c
o
nt
a
ins
k
e
yw
o
r
d
s
{
W
1
,
W
2
.
.
.
,
W
n
}
,
t
he
f
i
r
s
t
m
k
e
yw
o
r
d
s
(
w
he
r
e
1
≤
m
≤
n)
in
{
W
1
,
W
2
.
.
.
,
W
n
}
w
il
l
b
e
e
xp
l
o
r
e
d
in
p
r
e
v
i
o
u
s
k
e
yw
o
r
d
no
d
e
s
o
n
ly
if
t
he
r
e
a
r
e
m
nu
mb
e
r
o
f
k
e
yw
o
r
d
no
d
e
s
in
t
he
q
-
no
d
e
’
s
a
n
c
e
s
t
o
r
no
d
e
s
inc
lu
d
in
g
p
a
r
e
nt
no
d
e
.
S
o
,
o
n
ly
la
s
t
(
n
-
m
)
k
e
yw
o
r
d
s
f
o
r
e
a
c
h
q
u
e
r
y
w
i
l
l
be
s
t
o
r
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d
.
W
h
e
r
e
K
W
a
nd
S
P
d
e
no
t
e
k
e
y
w
o
r
d
no
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e
a
nd
s
p
a
t
i
a
l
no
d
e
r
e
s
p
e
c
t
iv
e
l
y
.
A
s
d
is
c
u
s
s
e
d
in
A
lg
o
r
i
t
h
m
1
,
t
he
g
r
e
e
d
y
a
p
p
r
o
a
c
h
is
u
s
e
d
f
o
r
t
he
k
e
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o
r
d
p
a
r
t
i
t
i
o
n
o
f
q
u
e
r
y
in
d
e
x
w
h
e
r
e
a
ll
t
h
e
k
e
yw
o
r
d
s
a
r
e
d
iv
id
e
d
int
o
c
p
a
r
t
i
t
i
o
ns
/
c
u
t
s
.
A
lg
o
r
i
t
h
m
2
d
e
s
c
r
ib
e
s
t
he
p
r
o
c
e
d
u
r
e
o
f
bu
i
ld
in
g
a
n
in
d
e
x
f
o
r
q
u
e
r
y
d
a
t
a
s
e
t
Q
w
he
r
e
w
he
r
e
k
e
yw
o
r
d
a
nd
s
p
a
t
ia
l
no
d
e
s
is
g
e
ne
r
a
t
e
d
u
s
in
g
c
o
s
t
m
o
d
e
l
.
T
he
d
u
m
m
y
c
u
t
/
c
e
ll
is
u
s
e
d
f
o
r
t
he
q
u
e
r
ie
s
if
t
he
nu
mb
e
r
o
f
k
e
yw
o
r
d
s
in
a
q
u
e
r
y
is
le
s
s
t
ha
n
t
he
o
f
f
s
e
t
o
f
t
he
p
a
r
t
i
t
i
o
n
o
r
t
he
r
e
g
i
o
n
o
f
t
he
q
u
e
r
y
c
o
nt
a
ins
t
he
r
e
g
i
o
n
o
f
t
he
s
p
a
t
i
a
l
no
d
e
.
T
he
o
b
je
c
t
s
e
a
r
c
h
in
g
i
s
d
o
ne
u
s
in
g
A
lg
o
r
i
t
h
m
3
o
n
q
u
e
r
y
in
d
e
x
w
he
r
e
t
he
s
e
a
r
c
h
in
g
is
d
o
ne
f
r
o
m
t
he
r
o
o
t
no
d
e
a
nd
e
ve
r
y
k
e
yw
o
r
d
in
t
he
inc
o
mi
n
g
o
b
je
c
t
is
s
e
a
r
c
h
e
d
in
e
a
c
h
p
a
r
t
i
t
i
o
n
o
f
k
e
yw
o
r
d
no
d
e
.
I
n
c
a
s
e
o
f
s
p
a
t
i
a
l
no
d
e
,
t
he
l
o
c
a
t
i
o
n
o
f
t
he
o
b
j
e
c
t
i
s
c
he
c
k
e
d
in
t
he
c
e
ll
s
o
f
s
p
a
t
i
a
l
no
d
e
.
F
i
g
u
r
e
3.
L
DA
P
t
r
e
e
f
o
r
que
r
y
i
nde
xi
ng
Evaluation Warning : The document was created with Spire.PDF for Python.
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
I
S
S
N:
2502
-
4752
E
ff
icie
nt
pr
oc
e
s
s
ing
of
c
onti
nuous
s
pati
al
-
tex
tual
q
ue
r
ies
ov
e
r
ge
o
-
tex
tual
…
(
K
alpana
V
ive
k
M
e
tr
e
)
1099
Al
go
r
i
t
hm
1:
Gr
e
e
d
y
M
e
t
h
o
d_
f
o
r
_ke
y
wo
r
d
P
a
r
t
i
t
i
o
n
(
KW
,
c
)
Input: KW: Set of
keywords,
c: fanout of node
i.e.,
cuts
Output: Pc: Partition with keywords with c cuts
1)
find
an
initial
solution
partition
Pc
which
partitions
KW
into
c
cuts
with
similar
weights;
2)
for (2<=j<=c) do
3)
for every keyword u lies between left
(C
j
-
1
) and right
(c
j
) do
4)
calculate C(Pc) // using cost model
5)
if
(lower C(Pc) is achieved)
6)
Update cuts C
j
-
1
and c
j
in Pc
7) return Pc
Al
go
r
i
t
hm
2:
I
n
de
x
_c
o
ns
t
r
uc
t
(
C
N
,
Q,
l
,
P
k
,
P
s
)
Input
Q
: Query dataset, C
N: current
node,
l
: offset for partitioning keywords
Ps
and
Pk:
flags
for
spatial
and
keywor
d
partitions
respectively,
thr:
threshol
d
v
alue for
storing queries in query node
Output: LDAP
-
Tree
1 if
(Pk is not true
and
Ps is not true)
or |Q| < thr
,
then
2
CN
is a query
-
node for set of queries
Q
3
return
4 k
-
cost = +∞
s
-
cost
= + ∞
5 if (Pk is true)
then
6
k
-
cost
compute cost for partition on keywords on
Q
having offset
l
7 if (
Ps
is true ) then
8
s
-
cost
compute cost for partition on spatial on
Q
9 if (Pk) is selected (i.e.,
k
-
cost < s
-
cost)
then
10
CN is a keyword
-
node
having offset
N
l
=
l
11
create dummy cut for Queries Q
d
where
|q.kw| <
l
12
for every child node B (i.e., cut) of node C
N
do
13
Q
B
=
Q
-
Q
d
14
Index _construct(B,
Q
B
.,
l
+ 1, Pk, Ps);
16 if C
N
is a spatial
-
node
17
Q
d
=
queries from
Q
contains N
r
18
create dummy cut for Queries Q
d
19
for every child node B (cell)
of node C
N
do
20
Q
B
=
Q
-
Q
d
21
index_construct(B, Q
B
,
l
, Pk, Ps);
Al
go
r
i
t
hm
3:
Obj
e
c
t
S
e
a
r
c
h
(
o,
s
,
C
N)
I
n
p
u
t
:
o
:
i
n
c
o
m
i
n
g
s
p
a
t
i
a
l
-
k
e
y
w
o
r
d
o
b
j
e
c
t
,
s
:
s
t
a
r
t
p
o
s
i
t
i
o
n
w
r
t
o
.
k
w
s
,
C
N
:
c
u
r
r
e
n
t
a
c
c
e
s
s
e
d
n
o
d
e
Output: A: Result set containing the matched and relevant queries for an object
1 if CN is a query
-
node then
2
check_queries(N)
3
update A
3 return
4 if CN is a keyword
-
node then
5
for s <= j <= o.kws do
6
find appropriate cut based on K
j
in (o.kws)
7
i
f
any cut is not explored then
8
ObjectSearch(o, j + 1, cut)
9 if exist (dummy_cell) then
10
ObjectSearch(0, s, dummy_cell)
11
else
12
find the grid that covers o.loc using cell structure;
13
ObjectSearch(0, s, cell)
14
if dummy_cut is present then
15
ObjectSearc
h(0, s, dummy_cut)
In the algorithm check_queries(), the following conditions are checked.
1)
The superset containment of keywords in queries to keywords in the object.
2)
The object location lies in the region of query and time constraint is checked.
3)
T
h
e
a
p
p
r
o
x
i
m
a
t
e
m
a
t
c
h
i
n
g
o
f
t
h
e
k
e
y
w
o
r
d
in
q
u
e
r
y
a
n
d
t
h
e
k
e
y
w
o
r
d
o
b
j
e
c
t
i
s
d
o
n
e
us
i
n
g
Levenshtein distance method at query node as well as keyword node for every query keyword.
4.
RE
S
UL
T
S
AN
D
DI
S
CU
S
S
I
ON
T
h
e
r
e
s
u
l
t
s
o
f
e
x
pe
r
im
e
n
t
a
l
e
v
a
l
ua
t
i
o
n
a
r
e
pr
e
s
e
n
t
e
d.
E
x
pe
r
i
m
e
n
t
a
l
S
e
t
up:
T
h
e
ge
o
-
ke
y
wo
r
d
-
b
a
s
e
d
s
e
r
vi
c
e
s
h
a
ve
b
e
e
n
f
r
e
que
n
t
l
y
u
s
e
d
i
n
n
u
m
e
r
o
us
a
pp
l
i
c
a
t
i
o
ns
e
.
g.
,
s
o
c
i
a
l
m
e
d
i
a
,
a
n
d
d
i
g
i
t
a
l
a
d
ve
r
t
i
s
i
ng.
T
o
e
v
a
l
u
a
t
e
t
h
e
d
i
s
c
u
s
s
e
d
i
nde
xi
ng
a
ppr
o
a
c
h
,
t
h
e
f
o
l
l
o
w
i
ng
da
t
a
s
e
t
s
a
r
e
us
e
d.
i)
A
I
S
da
t
a
s
e
t
c
o
n
t
a
i
n
s
t
h
e
g
e
o
-
l
o
c
a
t
i
o
ns
w
hi
c
h
a
r
e
t
a
ke
n
f
r
o
m
C
h
o
r
o
c
h
r
o
n
o
s
A
r
c
hiv
e
(
h
tt
p:/
/www
.
c
h
o
r
o
c
h
r
o
n
o
s
.
or
g)
[
3
5
]
.
Evaluation Warning : The document was created with Spire.PDF for Python.
I
S
S
N
:
2502
-
4752
I
n
do
n
e
s
i
a
n
J
E
l
e
c
E
n
g
&
C
o
m
p
S
c
i
,
Vo
l
.
25
,
N
o
.
2
,
F
e
b
r
ua
r
y
20
22
:
1094
-
1102
1100
ii
)
K
e
y
wo
r
ds
a
r
e
t
a
ke
n
f
r
o
m
Ne
ws
gr
o
ups
wi
t
h
a
b
o
ut
61
,
000
ke
y
w
o
r
ds
(
h
tt
p:/
/pe
o
pl
e
.
c
s
a
il
.
mi
t
.
e
du
l
j
r
e
nni
e
120N
e
ws
gr
o
ups
)
[
3
5
]
.
Obj
e
c
t
da
t
a
s
e
t
i
s
ge
n
e
r
a
t
e
d
us
i
ng
t
h
e
s
pa
t
i
a
l
l
o
c
a
t
i
o
ns
a
n
d
ke
y
wo
r
ds
f
r
o
m
v
o
c
a
b
u
l
a
r
y
(
Ne
ws
gr
o
ups
)
:
500
,
000
wi
t
h
1
-
9
ke
y
wo
r
ds
.
i
i
i
)
T
o
g
e
n
e
r
a
te
q
u
e
r
y
w
or
k
l
oa
d
:
100
,
0
0
0
s
p
a
ti
a
l
-
k
e
y
wor
d
ob
j
e
c
ts
f
r
om
th
e
da
ta
s
e
t
a
r
e
s
e
l
e
c
te
d
.
F
r
om
e
v
e
r
y
s
e
l
e
c
te
d
ob
j
e
c
t,
th
e
k
e
y
w
or
d
s
f
or
q
u
e
r
i
e
s
a
r
e
s
e
l
e
c
te
d
r
a
n
d
o
m
l
y
f
r
o
m
th
e
s
e
l
e
c
te
d
ob
j
e
c
ts
v
a
r
y
i
n
g
i
t
b
e
tw
e
e
n
1
a
n
d
5
.
T
h
e
q
u
e
r
y
r
e
gi
o
n
o
f
th
e
q
u
e
r
y
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1101
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[
1]
T
.
W
.
Y
a
n
a
nd
H
.
G
a
r
c
ía
-
M
o
l
in
a
,
“
I
nde
x
s
tr
u
c
tu
r
e
s
f
o
r
s
e
l
e
c
ti
ve
di
s
s
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mi
na
ti
o
n
of
in
f
or
ma
ti
o
n
unde
r
th
e
B
oo
l
e
a
n
m
o
de
l,
”
A
C
M
T
r
ans
ac
ti
ons
on Databa
s
e
Sy
s
t
e
m
s
, v
o
l.
19, n
o
. 2, pp.
332
–
364,
J
un. 1994, do
i:
10.1145/176567.17
6573.
[
2]
M.
-
H
.
P
a
r
k,
J
.
-
H
.
H
o
ng,
a
nd
S
.
-
B
.
C
ho
,
“
L
oc
a
ti
o
n
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B
a
s
e
d
R
e
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o
mm
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nda
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y
s
te
m
U
s
in
g
B
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y
e
s
ia
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U
s
e
r
’
s
P
r
e
f
e
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n
c
e
M
o
d
e
l
in
M
o
bi
l
e
D
e
v
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c
e
s
,”
i
n
U
bi
qui
to
us
I
nt
e
ll
ig
e
nc
e
and C
om
put
in
g
, B
e
r
li
n,
H
e
id
e
lb
e
r
g:
S
p
r
in
g
e
r
B
e
r
li
n
H
e
id
e
lb
e
r
g, pp. 1130
–
1139.
[
3]
L
. C
h
e
n,
G
. C
o
ng, a
nd X
. C
a
o
, “
A
n e
f
f
i
c
i
e
nt
qu
e
r
y
i
nd
e
x
in
g m
e
c
ha
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s
m f
or
f
il
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in
g ge
o
-
t
e
x
tu
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l
da
ta
,”
i
n
P
r
oc
e
e
di
ngs
o
f
t
he
2
013
in
te
r
nat
io
nal
c
onf
e
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e
nc
e
on M
anage
m
e
nt
o
f
dat
a
-
SI
G
M
O
D
’
13
, 2013, p. 749, do
i:
10.114
5/
2463676.2465328.
[
4]
G
.
L
i,
Y
.
W
a
ng,
T
.
W
a
ng,
a
nd
J
.
F
e
ng,
“
L
oc
a
ti
o
n
-
a
w
a
r
e
publ
is
h/
s
ubs
c
r
ib
e
,”
in
P
r
oc
e
e
di
ngs
of
th
e
19t
h
A
C
M
SI
G
K
D
D
in
te
r
nat
io
nal
c
onf
e
r
e
nc
e
on K
now
le
dge
di
s
c
ov
e
r
y
and data min
in
g
, A
ug. 2013, pp. 802
–
810, do
i:
10.1145/2487575.2
487617.
[5
]
S
.
H
e
l
me
r
a
n
d
G
.
M
o
e
r
ko
t
te
,
“
A
p
e
r
f
o
r
m
a
nc
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s
t
u
dy
o
f
f
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ty
,
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h
e
V
L
D
B
J
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[
6]
Z
.
H
me
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h,
H
.
K
o
ur
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,
V
.
C
hr
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s
,
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.
du
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ouz
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,
M
.
S
c
h
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ll
,
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nd
N
.
T
r
a
ve
r
s
,
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S
ubs
c
r
ip
ti
o
n
in
de
xe
s
f
o
r
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b
s
y
ndi
c
a
ti
o
n
s
y
s
t
e
ms
,”
i
n
P
r
oc
e
e
di
ngs
of
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he
15t
h I
nt
e
r
nat
io
nal
C
onf
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r
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nc
e
on E
x
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ng D
at
abas
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e
c
hnol
ogy
-
E
D
B
T
’
12
,
2
012,
p.
312, do
i:
10.1145/2247596.2
247634.
[
7]
Z
.
G
a
li
ć
,
M
.
B
a
r
a
novi
ć
,
K
.
K
r
i
ž
a
n
ov
ić
,
a
nd
E
.
M
e
š
k
ov
i
ć
,
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G
e
o
s
pa
ti
a
l
da
ta
s
tr
e
a
ms
:
F
o
r
ma
l
f
r
a
m
e
w
o
r
k
a
nd
im
pl
e
me
n
ta
ti
o
n,”
D
at
a &
K
now
le
dge
E
ngi
ne
e
r
in
g
, vo
l.
91, pp. 1
–
16, M
a
y
2014,
do
i:
10.1016/j
.da
ta
k.2014.02.002.
[
8]
S
.
E
o
m,
S
.
S
hi
n,
a
nd
K
.
-
H
.
L
e
e
,
“
S
pa
ti
o
t
e
mp
o
r
a
l
qu
e
r
y
pr
oc
e
s
s
in
g
f
o
r
s
e
ma
nt
i
c
da
ta
s
tr
e
a
m,”
in
P
r
oc
e
e
di
ngs
of
th
e
2015
I
E
E
E
9t
h
I
nt
e
r
nat
io
nal
C
on
f
e
r
e
nc
e
on
Se
m
ant
ic
C
om
put
in
g
(
I
E
E
E
I
C
SC
2015)
,
F
e
b.
2015,
pp.
290
–
297,
do
i:
10.1109/I
C
O
S
C
.2015.7050822.
[
9]
Z.
G
a
li
ć
, “
S
pa
ti
o
-
T
e
mp
o
r
a
l
D
a
ta
S
tr
e
a
ms
a
nd B
ig
D
a
ta
P
a
r
a
di
g
m,”
2016, pp. 47
–
69.
[
10]
Z
.
G
a
li
ć
,
E
.
M
e
š
k
ov
i
ć
,
a
nd
D
.
O
s
ma
nov
i
ć
,
“
D
is
tr
ib
ut
e
d
pr
oc
e
s
s
in
g
of
bi
g
m
o
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e
oI
nf
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al
.
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te
m
s
,
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X
.
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ng,
Y
.
Z
ha
ng,
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.
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ng,
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.
L
in
,
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nd
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.
W
a
ng,
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P
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o
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l
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ke
y
w
or
d
qu
e
r
i
e
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ove
r
s
tr
e
a
m,”
in
2015
I
E
E
E
31s
t
I
nt
e
r
nat
io
nal
C
onf
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e
nc
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K
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V
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M
e
tr
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a
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M
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U
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K
ha
r
a
t,
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c
a
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xe
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i
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us
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g
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a
ta
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A
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c
h,”
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nt
e
r
nat
io
nal
J
our
nal
of
E
ngi
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e
r
in
g &
T
e
c
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K
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V
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M
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tr
e
,
“
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o
nt
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ur
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nal
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put
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at
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duc
at
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T
U
R
C
O
M
A
T
)
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.
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[
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.
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n,
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.
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o
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V
L
D
B
E
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e
nt
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L
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M
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a
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H
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ie
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o
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pa
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E
E
E
T
r
ans
ac
ti
ons
on
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le
dge
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D
at
a E
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r
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or
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ki
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J
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H
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C
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po
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l
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s
,
A
.
M
a
r
ko
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e
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z
,
a
nd
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.
S
ue
l,
“
T
e
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t
v
s
.
s
pa
c
e
,”
in
P
r
oc
e
e
di
ngs
o
f
th
e
20t
h
A
C
M
in
te
r
nat
io
nal
c
onf
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r
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on I
nf
o
r
m
at
io
n and k
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m
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m
e
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J
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O
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G
k
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,
S
.
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o
na
s
s
e
n,
a
nd
K
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N
ør
v
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g
,
E
f
f
ic
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nt
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oc
e
s
s
in
g
o
f
to
p
-
k
s
pat
ia
l
k
e
y
w
or
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que
r
ie
s
,
I
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n
a
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.
S
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[
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C
he
ng
y
ua
n
Z
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Y
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g
Z
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e
nj
i
e
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ha
ng,
a
nd
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ue
mi
n
L
in
,
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n
ve
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te
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li
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a
r
qua
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r
e
e
:
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f
f
i
c
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e
nt
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o
p
k
s
pa
ti
a
l
k
e
y
w
o
r
d
s
e
a
r
c
h,”
in
2013
I
E
E
E
29t
h
I
n
te
r
nat
io
nal
C
onf
e
r
e
nc
e
on
D
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E
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v
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s
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e
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ks
,”
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A
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anc
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s
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D
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abas
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e
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E
D
B
T
2014:
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h
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nt
e
r
nat
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onf
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r
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nc
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x
te
ndi
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T
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N
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R
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w
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r
d
S
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a
r
c
h
o
n
S
pa
ti
a
l
D
a
ta
ba
s
e
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,”
in
2008
I
E
E
E
24t
h
I
nt
e
r
nat
io
nal
C
onf
e
r
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nc
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ba
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J
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i
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a
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K
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s
, a
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s
ha
, “
F
il
te
r
in
g a
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o
r
i
th
ms
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n
d i
mpl
e
m
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nt
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ti
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y
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r
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s
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t
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“
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xi
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oo
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a
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e
x
pr
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s
io
ns
,”
P
r
oc
e
e
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V
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D
B
E
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A
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J
a
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“
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tr
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e
,”
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P
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oc
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e
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th
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2011
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te
r
nat
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nal
c
onf
e
r
e
n
c
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M
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f
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r
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d
e
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r
oc
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e
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th
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V
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i
c
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e
nt
E
v
a
lu
a
ti
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e
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e
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r
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u
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s
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E
E
E
T
r
ans
ac
ti
ons
on
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publ
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f
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a
nno
ta
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w
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,”
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nuo
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m
ov
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t
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k
s
pa
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a
l
ke
y
w
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r
d
qu
e
r
y
p
r
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c
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s
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in
2011
I
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E
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27t
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n
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ic
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s
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gi
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c
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ns
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c
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or
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ng
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o
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-
K
s
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ti
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qu
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s
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P
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th
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A
C
M
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te
r
nat
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c
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r
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nc
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on
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nf
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m
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r
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K
e
y
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T
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p
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r
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,”
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E
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t
I
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r
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nal
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R
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N
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h
y
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i
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tu
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f
o
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e
o
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P
r
oc
e
e
di
ng
of
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e
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h
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C
M
c
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e
r
e
nc
e
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n
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:
10.1145/1645953.16
46188.
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,
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.
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688
-
699,
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i:
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C
D
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.2009.77.
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Evaluation Warning : The document was created with Spire.PDF for Python.